多重脑基因表达图像的统计分析

Statistical analysis of multiplex brain gene expression images.

作者信息

Ossadtchi Alex, Brown Vanessa M, Khan Arshad H, Cherry Simon R, Nichols Thomas E, Leahy Richard M, Smith Desmond J

机构信息

Department of Electrical Engineering, Signal and Image Processing Institute, School of Engineering, University of Southern California, Los Angeles 90089, USA.

出版信息

Neurochem Res. 2002 Oct;27(10):1113-21. doi: 10.1023/a:1020965107124.

DOI:10.1023/a:1020965107124

PMID:12462409

Abstract

Analysis of variance (ANOVA) was employed to investigate 9,000 gene expression patterns from brains of both normal mice and mice with a pharmacological model of Parkinson's disease (PD). The data set was obtained using voxelation, a method that allows high-throughput acquisition of 3D gene expression patterns through analysis of spatially registered voxels (cubes). This method produces multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. The ANOVA model was compared to the results from singular value decomposition (SVD) by using the first 42 singular vectors of the data matrix, a number equal to the rank of the ANOVA model. The ANOVA was also compared to the results from non-parametric statistics. Lastly, images were obtained for a subset of genes that emerged from the ANOVA as significant. The results suggest that ANOVA will be a valuable framework for insights into the large number of gene expression patterns obtained from voxelation.

摘要

采用方差分析（ANOVA）研究正常小鼠和患有帕金森病（PD）药理学模型小鼠大脑中的9000种基因表达模式。该数据集是通过体素化获得的，体素化是一种通过分析空间配准的体素（立方体）来高通量获取三维基因表达模式的方法。这种方法产生多个基因表达的体积图，类似于生物医学成像系统中重建的图像。通过使用数据矩阵的前42个奇异向量（该数量等于ANOVA模型的秩），将ANOVA模型与奇异值分解（SVD）的结果进行比较。还将ANOVA与非参数统计结果进行比较。最后，获得了ANOVA分析显示具有显著性的一部分基因的图像。结果表明，ANOVA将成为深入了解通过体素化获得的大量基因表达模式的有价值框架。